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Oct 2020
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Differential Analysis of N-glycopeptide Abundance and N-glycosylation Site Occupancy for Studying Protein N-glycosylation Dysregulation in Human Disease
用于研究人类疾病中蛋白质N-糖基化失调的N-糖肽丰度和N-糖基化位点的差异分析   

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Abstract

Protein N-glycosylation plays a vital role in diverse cellular processes, and dysregulated N-glycosylation is implicated in a variety of human diseases including neurodegenerative disorders and cancer. With recent advances in high-resolution mass spectrometry-based glycoproteomics technologies enabling large-scale N-glycoproteome profiling of disease and control samples, analysis of the large datasets has become a challenge. Here, we provide a protocol for the systems-level analysis of in vivo N-glycosylation sites on N-glycosylated proteins and their changes in human disease, such as Alzheimer's disease. The protocol includes quantitation and differential analysis of N-glycopeptide abundance, in addition to integrative N-glycoproteome and proteome data analyses, to determine disease-associated changes in N-glycosylation site occupancy and identify differentially N-glycosylated proteins in human disease versus control samples. This protocol can be modified and applied to study proteome-wide N-glycosylation alterations in response to different cellular stresses or pathophysiological states in other organisms or model systems.

Keywords: Protein N-glycosylation (蛋白质N -糖基化修饰), N-glycoproteomics (N-糖蛋白质组学), In vivo N-glycosylation sites (体内N-糖基化位点), N-glycoproteome profiling (N-糖蛋白组分析), N-glycosylation site occupancy (N-糖基化位点占用), Integrative glycoproteomics and proteomics (综合糖蛋白质组学和蛋白质组学), Alzheimer's disease (阿尔茨海默病), Mass spectrometry (质谱分析法)

Background

Protein N-glycosylation – the attachment of glycans to asparagine residues – is a major posttranslational modification for regulating many key biological processes from membrane trafficking and protein degradation to immune response and cell-cell communication (Moremen et al., 2012). The importance of proper N-glycosylation in human health is underscored by the finding that mutations in the protein N-glycosylation machinery components cause congenital disorders of glycosylation with multi-system abnormalities (Freeze et al., 2015). Furthermore, increasing evidence links aberrant protein N-glycosylation to various human diseases including cancer, diabetes, and neurodegenerative diseases (Schedin-Weiss et al., 2014; Pinho and Reis, 2015; Reily et al., 2019).


A mechanistic understanding of the roles of protein N-glycosylation in biology and pathophysiology requires the system-wide elucidation of in vivo N-glycosylation sites (N-glycosites) on N-glycosylated proteins (N-glycoproteins) in health and disease. Protein N-glycosylation sites typically have a canonical sequon N-X-S|T, where X represents any amino acid except proline. However, whether a sequon-containing site can be N-glycosylated in vivo is dependent on protein folding and localization, site availability, and accessibility to N-glycosylation enzymes (Moremen et al., 2012; Cherepanova et al., 2016); thus, the in vivo N-glycosylation sites on glycoproteins have to be determined experimentally. Furthermore, since stress and pathophysiological conditions can alter N-glycosylation site occupancy and expose the cryptic N-glycosites (i.e., normally unutilized N-glycosylation sequons) for N-glycosylation (Pinho and Reis, 2015; Cherepanova et al., 2016), it is important to determine disease-associated, site-specific changes in protein N-glycosylation site occupancy to gain mechanistic insights into N-glycosylation dysregulation and its roles in disease pathogenesis.


High-resolution mass spectrometry-based glycoproteomics technologies provide powerful tools for unbiased, large-scale, site-specific N-glycoproteome profiling analyses of complex biological samples. For example, in our recently published study (Zhang et al., 2020), we used an N-glycoproteomics workflow consisting of SDS-mediated protein extraction and filter-aided sample preparation (FASP) (Wisniewski, 2017), zwitterionic chromatography-hydrophilic interaction chromatography (ZIC-HILIC)-based glycopeptide enrichment (Ma et al., 2015), 18O-labeling of in vivo N-glycosylation sites (Kuster and Mann, 1999), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to characterize protein N-glycosylation in human Alzheimer's disease (AD) and control brains. We identified 4730 18O-labeled N-glycosite-containing peptides (hereafter referred to as N-glycopeptides) and mapped 2294 in vivo N-glycosylation sites on 1132 brain N-glycoproteins (Zhang et al., 2020).


With advanced N-glycoproteomics technologies enabling simultaneous, quantitative measurement of abundance profiles for thousands of N-glycopeptides and in vivo N-glycosylation sites, analysis of such large datasets has become a challenge. In our recent study (Zhang et al., 2020), we performed differential analysis of N-glycopeptide abundance and identified 118 N-glycopeptides with >1.3-fold change in N-glycopeptide abundance in AD as compared with control cases. By integrated analysis of our N-glycoproteome and proteome profiling data from the same brain samples (Zhang et al., 2018), we identified 77 N-glycosites on 60 N-glycoproteins with >1.3-fold changes in N-glycosylation site occupancy in AD versus control brains (Zhang et al., 2020). Furthermore, we performed qualitative assessment of whether an N-glycosite was exclusively occupied in either AD or control brains and identified 89 N-glycosites on 76 glycoproteins with a gain of N-glycosylation in AD and 12 N-glycosites on 11 glycoproteins with a complete loss of N-glycosylation in AD. In total, we identified 137 differentially N-glycosylated proteins in AD versus control brains, including 92 hyperglycosylated proteins containing N-glycosites with increased N-glycosylation site occupancy and/or a gain of N-glycosylation in AD, 39 hypoglycosylated proteins containing N-glycosites with decreased N-glycosylation site occupancy or a loss of N-glycosylation in AD, and 6 aberrantly glycosylated proteins containing both hyperglycosylated and hypoglycosylated N-glycosites. These analyses have identified disease signatures of altered N-glycopeptides, N-glycoproteins, and N-glycosylation site occupancy in AD and suggest new targets for AD biomarker development (Zhang et al., 2020).


Here, we provide a protocol that describes how to perform mass spectrometry-based, systems-level analysis of in vivo N-glycosylation sites on N-glycosylated proteins and their changes in human disease (Figure 1). The protocol also includes quantitation and differential analysis of N-glycopeptide abundance, in addition to integrative N-glycoproteome and proteome data analyses, to determine disease-associated changes in N-glycosylation site occupancy and identify differentially N-glycosylated proteins in human disease versus control samples. For more details on the use and execution of this protocol, please refer to our research article (Zhang et al., 2020). Similar strategies as described in this protocol should be broadly applicable to study proteome-wide N-glycosylation alterations in response to different cellular stresses or pathophysiological conditions in other organisms or model systems.



Figure 1. Overview of the experimental workflow. The experimental procedures for these steps are described in this protocol.


Materials and Reagents

  1. Microcon 30-kDa centrifugal filter device (Merck, MRCF0R030)

  2. Syringe, 3 ml (BD syringe)

  3. VWR universal low-retention pipet tips, 200 μl (VWR, catalog number: 76322-150)

  4. Bovine α2-HS-glycoprotein (fetuin) (Sigma, Millipore, catalog number: F3004-25MG)

  5. ZIC-HILIC resin (particle size 10 μm, pore size 200 Å; Merck SeQuant, UmeÅ, Sweden)

  6. H218O (Sigma-Aldrich, catalog number: 487090)

  7. PNGase F (New England Biolabs, catalog number: P0704S)

  8. Urea (Sigma, Millipore, catalog number: U1250)

  9. Sodium dodecyl sulfate (Sigma, Millipore, catalog number: 71725)

  10. DL-dithiothreitol (Sigma, Millipore, catalog number: D0632)

  11. Iodoacetamide (Thermo Scientific, catalog number: A39271)

  12. Ammonium bicarbonate (Sigma, Millipore, catalog number: 09830)

  13. Formic acid (Sigma, Millipore, catalog number: 5330020050)

  14. Sequencing grade modified trypsin (Promega, catalog number: V5111)

Equipment

  1. Benchmark heat block (Benchmark Scientific)

  2. Eppendorf 5424 microcentrifuge (Eppendorf)

  3. Mortar and pestle, 150 ml capacity, porcelain (Grainger)

  4. NanoDrop spectrophotometer and Quartz cuvettes (Thermo Fisher Scientific)

  5. 3MTM EmporeTM C8 extraction disk (Thermo Fisher Scientific)

  6. PierceTM C18 tips, 100 μl bed (Thermo Fisher Scientific, catalog number: 87784)

  7. Nano-LC UltiMate 3000 high-performance liquid chromatography system (Thermo Fisher Scientific)

  8. LTQ-Orbitrap Elite mass spectrometer (Thermo Fisher Scientific)

  9. EASY-Spray PepMap C18 column (length, 50 cm; particle size, 2 μm; pore size, 100 Å; Thermo Fisher Scientific)

  10. Savant SpeedVac SC210A Plus centrifuge (Thermo Fisher Scientific)

  11. Precision general purpose water bath (Thermo Fisher Scientific)

Software

  1. Proteome Discoverer version 1.4 or higher (Thermo Fisher Scientific, www.thermofisher.com)

  2. Microsoft Excel (https://www.microsoft.com)

  3. GraphPad Prism 7 (GraphPad, https://www.graphpad.com/scientific-software/prism/)

  4. MetaCore bioinformatics software (https://portal.genego.com)

Procedure

  1. Protein extraction and filter-aided sample preparation

    1. Homogenize frozen tissues by grinding with a mortar and pestle under liquid nitrogen. Lyse ground tissues (25 mg per human disease or control case) in 150 µl lysis buffer (4% SDS, 100 mM DTT, and 100 mM Tris-HCl, pH 7.6) at room temperature, followed by incubation at 95°C for 5 min as described (Wisniewski et al., 2009; Wisniewski, 2017). No protease inhibitors are added to the lysis buffer because the presence of SDS efficiently inactivates protease functions (Wisniewski et al., 2009).

    2. After cooling the homogenates to room temperature, perform centrifugation at 16,000 × g for 5 min to obtain supernatants containing extracted proteins.

    3. Determine the concentrations of total protein in protein extracts by UV spectrometry at 280 nm in a cuvette with a 10-mm pathlength using a NanoDrop spectrophotometer with an extinction coefficient of 1.1 for 0.1% (g/L) solution (Wisniewski et al., 2009).

    4. Spike each protein extract by pipetting a small volume of bovine α2-HS-glycoprotein (fetuin) stock solution (0.4 µg/µl bovine fetuin in lysis buffer) to reach a final concentration of 0.1% fetuin (µg/µg total protein). The spiked-in bovine fetuin protein serves as an internal control for technical variations during sample processing and analysis.

    5. Process the protein extracts as described (Zhang et al., 2018) according to the filter-aided sample preparation (FASP) protocol (Wisniewski et al., 2009; Wisniewski, 2017).

      1. Mix 30 µl protein extract with 200 µl UA solution (8 M urea in 100 mM Tris-HCl, pH 8.5).

      2. Transfer the mixture into a Microcon 30-kDa centrifugal filter unit (MRCF0R030, Merck) and centrifuge at 14,000 × g for 15 min. Discard the flow-through.

      3. Add 200 µl UA solution to the filter unit and repeat the centrifugation. Discard the flow-through.

      4. Add 100 μl UA solution containing 50 mM iodoacetamide to the filter unit and incubate in the dark for 30 min at room temperature.

      5. Centrifuge the filter unit at 14,000 × g for 10 min. Discard the flow-through.

      6. Add 100 μl UA solution to the filter unit and centrifuge again at 14,000 × g for 10 min. Repeat this step twice.

      7. Add 100 μl 50 mM NH4HCO3 to the filter unit and centrifuge again. Repeat this step twice. Discard the flow-through.

      8. Add 40 μl 50 mM NH4HCO3 solution containing sequencing grade trypsin (enzyme to protein ratio 1:100) in the filter unit and incubate at 37°C for 12 h.

      9. Elute the trypsin-digested peptides by adding 100 μl 50 mM NH4HCO3 followed by centrifugation at 14,000 × g for 10 min. Collect the flow-through. Repeat this step five times and combine the eluates.

      10. Add trifluoroacetic acid (TFA) to the peptide solution to a final concentration of 0.5% TFA.

      11. Desalt the peptide samples using PierceTM C18 tips following the manufacturer’s protocol.

    6. Determine the concentration of the purified peptides in each sample by UV spectrometry at 280 nm in a cuvette with a 10-mm pathlength using a NanoDrop spectrophotometer with an extinction coefficient of 1.1 for 0.1% (g/L) solution at 280 nm (Wisniewski et al., 2009).

    7. Dry the peptide samples completely in a SpeedVac vacuum concentrator at room temperature for 3-6 h.


  2. ZIC-HILIC-based glycopeptide enrichment

    1. Prepare ZIC-HILIC microcolumns as described (Ma et al., 2015) by adding a slurry of ZIC-HILIC resin (10 mg) in 100 µl acetonitrile (ACN) to 200-µl tips plugged with a 3MTM EmporeTM C8 extraction disk at the bottom of each tip (Figure 2A).

    2. Use a 3-ml syringe to generate backpressure inside the tip (Figure 2B). Slowly push down the plunger to allow the solvent to flow through the ZIC-HILIC column without causing resin compression.



      Figure 2. ZIC-HILIC microcolumn. A. ZIC-HILIC microcolumn in a 200-µl tip plugged with a 3MTM EmporeTM C8 extraction disk. B. Use of a 3-ml syringe to aid the flow through the ZIC-HILIC column.


    3. Equilibrate the ZIC-HILIC microcolumn by adding 100 µl binding buffer [80% ACN and 5% formic acid (FA)] and slowly pushing the solution down using a 3-ml syringe as described in Step B2.

    4. Reconstitute dry peptides (100 µg peptides per sample) in 200 µl binding buffer.

    5. Load the reconstituted peptide samples onto pre-equilibrated ZIC-HILIC microcolumns. Slowly push the solution down using a 3-ml syringe as described in Step B2. Discard the flow-through.

    6. Wash each ZIC-HILIC microcolumn five times with 100 µl binding buffer, using the syringe to slowly push the solution down as described in Step B2. Discard the flow-through.

    7. Add 80 µl elution buffer (99.5% H2O and 0.5% FA) to the ZIC-HILIC microcolumn, and slowly push down using the syringe to elute the bound glycopeptides into a 1.5-ml tube. Repeat this step three times and collect the eluates in the same tube.

    8. Dry the glycopeptide samples completely in the SpeedVac concentrator at room temperature for 3-6 h.


  3. 18O-labeling of in vivo N-glycosylation sites

    1. Reconstitute dry glycopeptides in 50 µl 50 mM NH4HCO3 prepared with H218O.

    2. Add 0.5 µl (250 units) PNGase F to each sample tube and incubate in a water bath at 37°C overnight to allow 18O-labeling of the asparagine residues at in vivo N-glycosylation sites as described in Kuster and Mann (1999).

    3. Desalt the 18O-labeled peptide samples using self-packed C18 ZipTips or PierceTM C18 tips following the manufacturer’s protocol.

    4. Dry the 18O-labeled peptides samples completely in the SpeedVac concentrator at room temperature for 3-6 h.


  4. LC-MS/MS analysis of 18O-labeled N-glycosite-containing peptides

    1. Reconstitute dry 18O-labeled peptides (2 µg per sample) in 5 µl 0.1% fomic acid (FA).

    2. Separate peptides of each sample as described in Zhang et al. (2018) by online reversed phase-HPLC fractionation on an EASY-Spray PepMap C18 column, using a 240-min gradient from 2% to 50% solvent B at a flow rate of 300 nl/min (mobile phase A, 1.95% ACN, 97.95% H2O, 0.1% FA; mobile phase B, 79.95% ACN, 19.95% H2O, 0.1% FA).

    3. Perform mass spectrometric analysis as described in Zhang et al. (2018) using data-dependent acquisition with full MS scans (m/z range, 375-1600; automatic gain control target, 1,000,000 ions; resolution at 400 m/z, 60,000; maximum ion accumulation time, 50 ms) in the Orbitrap mass analyzer. The ten most intense ions in each full scan are fragmented by collision-induced dissociation with a maximum ion accumulation time of 100 ms in the LTQ mass spectrometer (automatic gain control target value, 10,000) to acquire MS/MS spectra.

Data analysis

  1. Database search and quantitation of N-glycopeptide abundance

    1. Analyze LC-MS/MS raw data files using Proteome Discoverer and search the data against the human UniProt TrEMBL database (2016_02 Release, 20,198 reviewed entries) plus the bovine α2-HS-glycoprotein (fetuin).

    2. Perform the database search using the following parameters: 20-ppm precursor ion mass tolerance; 0.5-Da fragment ion mass tolerance; trypsin digestion with up to two missed cleavages; fixed modification: cysteine carbamidomethylation (+57.0215 Da); variable modifications: asparagine deamidation in H218O (18O tag of Asn, +2.9890 Da), asparagine and glutamine deamidation (+0.9840 Da), methionine oxidation (+15.9949 Da), and N-terminal acetylation (+42.0106 Da). Set the false discovery rate (FDR) to 1%.

    3. Select and accept the peptides with an 18O-tagged asparagine residue within the N-glycosylation sequon N-X-S|T|C (X ≠ P) as the in vivo N-glycosite-containing peptides (referred to as N-glycopeptides).

    4. Perform quantitative analysis of N-glycopeptide abundance using Proteome Discoverer to quantitate the peak area (i.e., area under the curve) of each 18O-labeled N-glycosite-containing peptide.

    5. Determine the normalized N-glycopeptide abundance by normalizing the peak area of each 18O-labeled N-glycosite-containing peptide to the corresponding peak area of the 18O-labeled internal standard N-glycopeptide KLCPDCPLLAPLN(18O)DSR derived from the spiked-in bovine fetuin protein in each sample.


  2. Differential analysis of N-glycopeptide abundance

    1. Since a limitation of quantitative N-glycoproteomics is that glycopeptide identifications or abundance values can be missing from some samples (Karpievitch et al., 2012), only the N-glycopeptides with valid abundance values detected in ≥50% of disease or control samples are included in the differential analysis.

    2. Perform differential analysis of N-glycopeptide abundance in Microsoft Excel using an unpaired Student’s t-test to compare the normalized N-glycopeptide abundance values for each 18O-labeled N-glycosite-containing peptide in disease samples with those values in control samples.

    3. Identify N-glycopeptides with significantly altered, normalized N-glycopeptide abundance in the disease state using the thresholds of ±1.3-fold change over the control group (i.e., disease/control ratio >1.3 or <0.77) and P < 0.05.

    4. Generate a volcano plot using GraphPad Prism to visualize the results of the differential analysis of N-glycopeptide abundance (e.g., Figure 2A in Zhang et al., 2020).


  3. Integrative N-glycoproteomics and proteomics analysis of N-glycosylation site occupancy

    1. Perform integrative analysis by comparing the N-glycoproteome data with the proteome data from the same samples measured using the same instrument for LC-MS/MS analysis.

    2. Analyze the proteome data by performing differential expression analysis as described in Zhang et al. (2018) to identify proteins with significantly altered normalized protein abundance in the disease state using the thresholds of ±1.3-fold change over the control group and P < 0.05.

    3. Determine the fold change in N-glycosylation site occupancy for each N-glycosite in disease versus control as the fold change in the normalized N-glycopeptide abundance of the N-glycosite-containing peptide divided by the fold change in the normalized protein abundance of the corresponding glycoprotein.

    4. Identify N-glycosites with altered N-glycosylation site occupancy in the disease state using the thresholds of ±1.3-fold change in N-glycosylation site occupancy in disease versus control.

    5. Perform qualitative assessment of whether an N-glycosite is exclusively occupied in either disease or control samples to identify N-glycosites with a complete loss or gain of N-glycosylation in the disease state.


  4. Identification of differentially N-glycosylated proteins and dysregulated N-glycosylation-affected processes

    1. Identify differentially N-glycosylated proteins in a disease as the N-glycoproteins containing in vivo N-glycosites with altered N-glycosylation site occupancy and/or a complete loss or gain of N-glycosylation in the disease state.

    2. Define hyperglycosylated proteins in a disease as the N-glycoproteins containing N-glycosites with increased N-glycosylation site occupancy and/or a gain of N-glycosylation in the disease state.

    3. Define hypoglycosylated proteins in a disease as the N-glycoproteins containing N-glycosites with decreased N-glycosylation site occupancy and/or a complete loss of N-glycosylation in the disease state.

    4. Define aberrantly N-glycosylated proteins as the N-glycoproteins containing both hyper-glycosylated and hypoglycosylated N-glycosites.

    5. Perform Gene Ontology (GO) enrichment analysis of the generated datasets of differentially N-glycosylated proteins using the MetaCore bioinformatics software as described in Zhang et al. (2020) to reveal dysregulated N-glycosylation-affected biological processes in the disease state.

Acknowledgments

This work was supported by the National Institutes of Health (NIH) Grants RF1AG057965 (to L.L.) and R56 AG059714 (to L.S.C.). This protocol was adapted from our recently published research paper (DOI: 10.1126/sciadv.abc5802). The Emory Center for Neurodegenerative Disease Brain Bank was supported in part by NIH Grants P50 AG025688 and P30 NS055077.

Competing interests

The authors declare that they have no competing interests.

Ethics

Research related to this work was performed in accordance with the NIH guidelines for research involving human tissues and with the ethical standards and principles of the Declaration of Helsinki. Human postmortem brain tissues were obtained from the Emory Center for Neurodegenerative Disease Brain Bank, and brain tissues were acquired with Institutional Review Board (IRB) approval and informed consent from the subject or their family.

References

  1. Cherepanova, N., Shrimal, S. and Gilmore, R. (2016). N-linked glycosylation and homeostasis of the endoplasmic reticulum. Curr Opin Cell Biol 41: 57-65.
  2. Freeze, H.H., Eklund, E.A., Ng, B.G. and Patterson, M.C. (2015). Neurological aspects of human glycosylation disorders. Annu Rev Neurosci 38: 105-125.
  3. Karpievitch, Y.V., Dabney, A.R. and Smith, R.D. (2012). Normalization and missing value imputation for label-free LC-MS analysis. BMC Bioinformatics 13 Suppl 16: S5.
  4. Kuster, B. and Mann, M. (1999). 18O-labeling of N-glycosylation sites to improve the identification of gel-separated glycoproteins using peptide mass mapping and database searching. Anal Chem 71(7): 1431-1440.
  5. Ma, C., Qu, J., Meisner, J., Zhao, X., Li, X., Wu, Z., Zhu, H., Yu, Z., Li, L., Guo, Y., Song, J. and Wang, P.G. (2015). Convenient and Precise Strategy for Mapping N-Glycosylation Sites Using Microwave-Assisted Acid Hydrolysis and Characteristic Ions Recognition. Anal Chem 87(15): 7833-7839.
  6. Moremen, K.W., Tiemeyer, M. and Nairn, A.V. (2012). Vertebrate protein glycosylation: diversity, synthesis and function. Nat Rev Mol Cell Biol 13(7): 448-462.
  7. Pinho, S.S. and Reis, C.A. (2015). Glycosylation in cancer: mechanisms and clinical implications. Nat Rev Cancer 15(9): 540-555.
  8. Reily, C., Stewart, T.J., Renfrow, M.B. and Novak, J. (2019). Glycosylation in health and disease. Nat Rev Nephrol 15(6): 346-366.
  9. Schedin-Weiss, S., Winblad, B. and Tjernberg, L.O. (2014). The role of protein glycosylation in Alzheimer disease. FEBS J 281(1): 46-62.
  10. Wisniewski, J.R. (2017). Filter-Aided Sample Preparation: The Versatile and Efficient Method for Proteomic Analysis. Methods Enzymol 585: 15-27.
  11. Wisniewski, J.R., Zougman, A., Nagaraj, N. and Mann, M. (2009). Universal sample preparation method for proteome analysis. Nat Methods 6(5): 359-362.
  12. Zhang, Q., Ma, C., Chin, L.S. and Li, L. (2020). Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer's disease. Sci Adv 6(40).
  13. Zhang, Q., Ma, C., Gearing, M., Wang, P.G., Chin, L.S. and Li, L. (2018). Integrated proteomics and network analysis identifies protein hubs and network alterations in Alzheimer's disease. Acta Neuropathol Commun 6(1): 19.

简介

[摘要]蛋白质N-糖基化起着一个在至关重要的作用不同的细胞过程,以及失调的N-糖基化是在多种人类疾病,包括神经变性疾病和癌症有关。随着高分辨质谱最新进展为基础的分析糖蛋白组学小号的技术实现大规模的N-糖蛋白质组分析疾病和对照样品,ANALY的姐姐大数据集已成为一个挑战。在这里,我们提供了一个协议,用于对 N-糖基化蛋白的体内N-糖基化位点及其在人类疾病(如阿尔茨海默病)中的变化进行系统级分析。该协议包括孔定量吨通货膨胀和差的分析的N-糖肽丰度,除了我ntegrative的N- glycoproteome和蛋白质组数据的分析,以确定在疾病相关的变化N-糖基化位点占据,并确定在人类疾病中差异N-糖基化蛋白与对照样品相比。该协议可以被修改和应用到研究全蛋白质组N-糖基化的改变响应于不同的细胞应激ES在其他生物体或模型系统或病理生理状态。


[背景]蛋白质 N-糖基化 -所述的附接聚糖至天冬酰胺残基-是一个主要用于调节从膜运输和蛋白质降解许多关键的生物学过程来翻译后修饰免疫应答和细胞-细胞通讯(Moremen等人。,2012) 。蛋白质 N-糖基化机制组件的突变导致先天性糖基化障碍和多系统异常(Freeze等,2015)的发现强调了适当N-糖基化对人类健康的重要性。此外,越来越多的证据链路小号异常蛋白N-糖基化的各种人类疾病,包括癌症,糖尿病,和神经变性疾病(Schedin -外斯等人,2014;皮尼奥和雷斯,2015;莱利。等人,2019) 。

对蛋白质N-糖基化在生物学和病理生理学中的作用的机制理解需要对健康和疾病中N-糖基化蛋白质(N-糖蛋白) 的体内N-糖基化位点 (N-糖基化位点) 进行系统范围的阐明。蛋白质 N-糖基化位点通常具有标准序列N-X- S|T ,其中 X 代表除脯氨酸以外的任何氨基酸。然而,无论是序列子含场地可以N-糖基化在体内是依赖于蛋白质折叠和定位,站点的可用性,和可访问到的N-糖基化酶(Moremen等人。,2012 ; Cherepanova等人。,2016 ); 吨HUS,所述体内N-糖基化的位点的糖蛋白,必须通过实验确定。此外,由于应力和病理生理条件下可以改变N-糖基化位点占据并暴露隐蔽的N- glycosites(即,通常未被利用N-糖基化序列肽段N-糖基化)(皮尼奥和雷斯,2015; Cherepanova等人。,2016) ,重要的是确定蛋白质N-糖基化位点占有率的疾病相关的、位点特异性的变化,以获得对 N-糖基化失调及其在疾病发病机制中的作用的机制见解。

基于高分辨率质谱的糖蛋白质组学技术为复杂生物样品的无偏、大规模、位点特异性N-糖蛋白质组谱分析提供了强大的工具。例如,在我们最近发表的研究(Zhang et al . , 2020) 中,我们使用了 N-糖蛋白质组学工作流程,包括 SDS 介导的蛋白质提取和过滤辅助样品制备 (FASP) (Wisniewski, 2017) 、两性离子色谱-亲水相互作用色谱法(ZIC-HILIC)基的糖肽的富集(马等人。,2015) ,18的O形标记的体内N-糖基化位点(库斯特和Mann,1999) ,和液相色谱-串联质谱(LC-MS /MS) 来表征人类阿尔茨海默病(AD) 和控制大脑中的蛋白质N-糖基化。我们鉴定4730 18 O形标记的含N glycosite肽(以下简称为的N-糖肽)和映射2294体内N-糖基化位点上1132脑的N-糖蛋白(张等人。,2020) 。

凭借先进的N- glycoproteomic小号技术实现的同时,定量测量丰型材千的N-糖肽和体内N-糖基化位点,ANALY的SIS如此大的数据集已成为一个挑战。在我们最近的研究(张等人。,2020) ,我们进行的N-差分分析的糖肽的丰度和识别118的N-糖肽与在N-糖肽丰度> 1.3倍的变化在AD作为比较用对照病例。用i ntegrated我们的N- glycoproteome的分析和蛋白质组剖析来自同一脑样品数据(章等人。,2018) ,我们鉴定77的N- glycosites 60的N-糖蛋白具有> 1.3倍的变化N-糖基化位点占据在AD与对照脑中(张等人。,2020) 。此外,我们对一个n N-糖位点是否仅在 AD 或对照大脑中进行了定性评估,并确定了 76 个糖蛋白上的 89 个 N-糖位点在 AD 中获得了 N-糖基化,11 个糖蛋白上的 12 个 N-糖位点具有AD 中 N-糖基化的完全丧失。总的来说,我们在 AD 与对照大脑中鉴定了 137种不同的 N-糖基化蛋白质,包括 92 种含有N-糖基化位点的高糖基化蛋白质,其N-糖基化位点占有率增加和/或 AD 中的 N-糖基化增加,39 种含有N-糖基化的低糖基化蛋白质AD 中 N-糖基化位点占有率降低或 N-糖基化缺失的糖基化蛋白,以及 6 种含有高糖基化和低糖基化N-糖基化蛋白的异常糖基化蛋白。这些分析已经确定的疾病签名改变的N-糖,N-二糖蛋白和N-糖基化位点占用AD和建议为AD生物标志物的发展新目标(张等人。,2020) 。

在这里,我们提供了一个协议,描述了如何对 N-糖基化蛋白质的体内N-糖基化位点及其在人类疾病中的变化进行基于质谱的系统级分析(图 1)。该协议还包括孔定量吨通货膨胀和差的N-糖肽丰度的分析,除了我ntegrative的N- glycoproteome一个第二蛋白质组数据的分析,以确定疾病-在相关的变化N-糖基化位点占据,并确定在差分N-糖基化蛋白人类疾病与对照样本。有关该协议的使用和执行的更多详细信息,请参阅我们的研究文章(Zhang et al . , 2020) 。相似的策略如在该协议描述应该广泛地适用于研究全蛋白质组N-糖基化的改变响应于不同的细胞应激ES或在其他生物体或模型系统的病理生理条件。


图 1.实验工作流程概述。本协议描述了这些步骤的实验程序。

关键字:蛋白质N -糖基化修饰, N-糖蛋白质组学, 体内N-糖基化位点, N-糖蛋白组分析, N-糖基化位点占用, 综合糖蛋白质组学和蛋白质组学, 阿尔茨海默病, 质谱分析法



材料和试剂


1、Microcon 30-kDa离心过滤装置(默克,MRCF0R030)     

2.注射器,3 毫升(BD 注射器)     

3. VWR普遍低-保持移液管尖端,200 μ升(VWR,目录号:76322-150)     

4.牛       α 2-HS-糖蛋白(胎球蛋白)(Sigma,Millipore,目录号:F3004-25MG)


5. ZIC-HILIC树脂(颗粒尺寸为10μm,孔径200埃;默克SeQuant ,默奥,瑞典)     

6. H 2 18 O(Sigma-Aldrich,目录号:487090 )     

7. PNGase F(New England Biolabs,目录号:P0704S)     

8.尿素(Sigma,Millipore,目录号:U1250 )     

9.十二烷基硫酸钠(Sigma,Millipore,目录号:71725)     

10. DL- ð ithiothreitol(Sigma公司,Millipore公司,目录号:D0632 ) 

11.碘乙酰胺(Thermo Scientific,目录号:A39271) 

12.碳酸氢铵(Sigma,Millipore,目录号:09830) 

13.甲酸(Sigma,Millipore,目录号:5330020050) 

14.测序级改良吨rypsin(Promega公司,目录号:V5111) 



设备


基准加热块(Benchmark Scientific )
Eppendorf 5424 m微量离心机 (Eppendorf)
研钵和杵,150米升容量,瓷器(固安捷)
NanoDrop分光光度计和石英比色皿( Thermo Fisher Scientific )
3M TM Empore TM C8 萃取盘(Thermo Fisher Scientific )
皮尔斯TM C18提示,100 μ升床(热Fisher Scientific公司,目录号:87784 )
Nano-LC UltiMate 3000 高效液相色谱系统(赛默飞世尔科技)
LTQ-Orbitrap Elite 质谱仪(赛默飞世尔科技)
EASY-Spray PepMap C18 柱(长度,50 cm;粒径,2 μm;孔径,100 Å;Thermo Fisher Scientific )
学者SpeedVac中SC210A加Ç entrifuge (赛默飞世尔科技)
精密通用水浴(Thermo Fisher Scientific )


软件


Proteome Discoverer 1.4 或更高版本(Thermo Fisher Scientific,www.thermofisher.com)
Microsoft Excel ( https://www.microsoft.com )
Graph P ad Prism 7(GraphPad,https ://www.graphpad.com/scientific-software/prism/ )
MetaCore 生物信息学软件 ( https://portal.genego.com )


程序


蛋白质提取和过滤辅助样品制备
在液氮下用研钵和研杵研磨均匀冷冻组织。在室温下在 150 µl裂解缓冲液(4% SDS、100 mM DTT 和 100 mM Tris - HCl,pH 7.6)中裂解研磨组织(每个人类疾病或对照病例 25 mg ),然后在 95°C 下孵育5 分钟,如所述(Wisniewski等人,2009 年;Wisniewski,2017 年)。没有蛋白酶抑制剂添加到裂解缓冲液中,因为 SDS 的存在有效地灭活了蛋白酶功能(Wisniewski等,2009)。
将匀浆冷却至室温后,以 16,000 × g离心5 分钟以获得含有提取蛋白质的上清液。 
确定总蛋白质的蛋白质提取物中的浓度通过UV光谱测定法在280nm在比色杯中使用10 -毫米光程使用纳米滴分光光度计用1.1的消光系数为0.1%(克/ L)溶液(的Wisniewski等人,2009。 ) 。
穗每个蛋白提取物通过吸取牛小体积的α 2-HS-糖蛋白(胎球蛋白)储备溶液(0.4 μ克/ μ升牛胎球蛋白在裂解缓冲液),以达到0.1%的胎球蛋白的最终浓度(μ克/ μ克总蛋白质)。加标入牛胎球蛋白蛋白质用作内部控制过程中的技术变化的样品处理和分析我秒。
根据过滤辅助样品制备(FASP) 协议(Wisniewski等人,2009 年;Wisniewski,2017年),按照所述(Zhang等人,2018 年)处理蛋白质提取物。
将 30 µl 蛋白质提取物与 200 µl UA 溶液(100 mM Tris-HCl 中的 8 M 尿素,pH 8.5 )混合。
将混合物转移到一个的Microcon 30 kDa的离心过滤装置(MRCF0R030,Merck)和离心机以14,000 ×克FO R 15分钟。D放弃流通。
将 200 µl UA 溶液添加到过滤器单元并重复离心。D放弃流通。
将 100 μl含有 50 mM 碘乙酰胺的 UA 溶液添加到过滤器单元中,并在室温下在黑暗中孵育30 分钟。
将过滤器单元以 14,000 × g离心10分钟。D放弃流通。
向过滤器中加入 100 μl UA 溶液,再次以 14,000 × g离心10 分钟。重复此步骤两次。
将 100 μl 50 mM NH 4 HCO 3 添加到过滤器中并再次离心。重复此步骤两次。D放弃流通。
在过滤器中加入 40 μl 50 mM NH 4 H CO 3溶液,其中含有测序级胰蛋白酶(酶蛋白比 1:100),37°C 孵育 12 小时。
洗脱通过添加100胰蛋白酶消化的肽微升50毫NH 4 HCO 3在14000,然后离心×克为10分钟。Ç ollect的流过。重复此步骤五次并合并洗脱液。
将三氟乙酸 (TFA) 添加到肽溶液中,最终浓度为0.5% TFA。
脱盐肽使用样品皮尔斯TM C18提示以下的制造ř的协议。
确定在280nm处每个样品中的纯化的肽通过UV光谱测定法的浓度在比色杯中使用10 -毫米光程使用纳米滴分光光度计用的1.1 0.1%(克/升)溶液的消光系数在280nm处(的Wisniewski等人,2009年)。
干燥该肽样品完全在SpeedVac中在室温下真空浓缩为3 - 6小时。


基于 ZIC-HILIC 的糖肽富集
制备ZIC-HILIC微柱如所描述的(马等人,2015年。)通过添加的浆料ZIC-HILIC树脂(10毫克)在100μl乙腈(ACN)至200 -微升提示封孔用3M TM EMPORE TM C8萃取磁盘在每个尖端的底部 (图 2A)。
使用3 -毫升注射器以产生尖端内背压(图2B) 。小号低向下推动柱塞,以允许溶剂通过流ZIC-HILIC而不引起树脂压缩列。




图 2. ZIC-HILIC微柱。一个。ZIC-HILIC在微柱200 -微升尖端插入用3M TM EMPORE TM C8萃取磁盘。乙。使用 3 - ml 注射器帮助流过ZIC-HILIC柱。


平衡的ZIC-HILIC通过添加微柱100 μ升结合缓冲液[80%ACN和5%甲酸酸(FA)] ,缓慢推溶液向下使用一个3 -毫升注射器如步骤B2中所述。
重新构建干肽200(每个样品100种微克肽)μ升结合缓冲液。
将重组肽样品加载到预先平衡的ZIC-HILIC微柱上。慢慢推溶液向下使用一个3 -毫升注射器如步骤B2中所述。D放弃流通。
用 100 µl 结合缓冲液清洗每个ZIC-HILIC微柱五次,使用注射器将溶液缓慢向下推,如步骤 B2 中所述。D放弃流通。
添加80μl的洗脱缓冲液(99.5%H 2 O和0.5%FA)到所述ZIC-HILIC微柱,并且慢慢地压低使用注射器洗脱结合的糖肽为1.5 -毫升管。重复此步骤3 次并在同一管中收集洗脱液。
ð RY的糖肽样品完全在SpeedVac中在室温下浓缩为3 - 6小时。


18 O-标记体内N-糖基化位点
重新构建干燥在50糖肽μ升的50mM NH 4 HCO 3用H制备2 18 O.
添加0.5 μ升(250个单位)PNG酶F到每个样品管和孵化在一个水浴在37℃下过夜,以使18的天冬酰胺残基的O形标记在体内N-糖基化位点如所描述的在(库斯特和Mann, 1999) 。
脱盐18 O形标记的肽用样品的自填充C18 ZipTips或皮尔斯TM C18提示以下的制造ř的协议。
d RY的18 O形标记的肽样品完全在SpeedVac中在室温下浓缩为3 - 6小时。


LC-MS/MS 分析18 个O 标记的含 N-糖苷点的肽
重新构建干18 O形标记的肽(2 μ克每样品)在5 μ升0.1%fomic酸(FA)。
如Zhang等人所述,分离每个样品的肽。( 2018)通过在EASY-Spray PepMap C18 色谱柱上进行在线反相-HPLC 分馏,使用 240 分钟梯度,从 2% 到 50% 溶剂 B,流速为 300 n l /min(流动相 A,1.95% ACN,97.95% H 2 O,0.1% FA;流动相 B,79.95% ACN,19.95% H 2 O,0.1% FA)。
按照Zhang等人的描述进行质谱分析。( 2018 ) 在 Orbitrap 中使用全 MS 扫描(m/z 范围,375 - 1600;自动增益控制目标,1,000,000 个离子;400 m/z 处的分辨率,60,000;最大离子累积时间,50 ms)进行数据相关采集质量分析仪。十个最强离子在每个全扫描是通过碰撞诱导解离与分段一在LTQ质谱仪100毫秒最大值离子累积时间(自动增益控制的目标值,10,000)来获取MS / MS谱。 


数据分析


数据库搜索和孔定量牛逼的N-糖肽丰通货膨胀
分析使用蛋白质组发现者LC-MS / MS原始数据文件,并搜索对数据的人的UniProt TrEMBL的数据库(2016_02推出,20198个审查条目)加上所述牛 α 2-HS-糖蛋白(胎球蛋白)。
执行该数据库搜索使用以下参数:20-ppm的前体离子质量公差; 0.5-Da 碎片离子质量耐受性;胰蛋白酶消化,最多有两个缺失的裂解;固定修饰:半胱氨酸氨基甲酰甲基化(+57.0215 Da);可变修饰:H 2 18 O 中的天冬酰胺脱酰胺(Asn 的18 O 标签,+2.9890 Da)、天冬酰胺和谷氨酰胺脱酰胺(+0.9840 Da)、甲硫氨酸氧化(+15.9949 Da)和 N 端乙酰化(+42.0106 Da) . 将错误发现率 (FDR) 设置为 1%。
选择和接受与所述肽的18的N-糖基化序列子内O形标记的天冬酰胺残NXS | T | C(X≠P)作为体内N- glycosite -含有肽(称为n-糖肽)。
执行的定量分析的N-糖肽使用蛋白质组发现者到孔定量丰度泰特的峰面积(即每个中,曲线下面积)18 O形标记的含有N- glycosite肽。 
通过归一化确定归一化的N-糖肽丰度的峰面积每18 O-标记的含N glycosite肽到的相应的峰面积的18 O形标记的内标的N-糖肽KLCPDCPLLAPLN(18 O)DSR从掺料衍生- 每个样品中的牛胎球蛋白蛋白。


N-糖肽丰度的差异分析
由于q的限制的定量的N-二glycoproteomics是糖肽标识或丰度值可以缺少从一些样品(Karpievitch等人,2012) ,只有N-二糖肽与有效丰度值检测 ≥疾病或对照样品的50%被包括在所述差分分析。 
执行的N-糖肽丰度的差异分析Microsoft Excel中使用的非配对吨-试验为比较归一化的N-糖肽丰度值每个18 O形标记的含N glycosite-与对照样品中的那些值疾病样品中的肽。
鉴定的N-糖肽与显著改变,归一化的N-糖肽丰度在疾病状态使用±相对于对照组(1.3倍的变化的阈值,即,疾病/控制比> 1.3或<0.77)和P <0.05。
产生一个使用Graph火山图P广告棱镜以可视化的结果的的N-糖肽丰度的差异分析(例如,图2A中张等人,2020 )。


我ntegrative N- glycoproteomic小号和蛋白质组学小号N-糖基化位点占据的分析
执行I由N glycoproteome数据与所述比较ntegrative分析从MEAS的相同样品的蛋白质组数据使用ured的对同一仪器LC-MS / MS分析。
分析通过执行蛋白质组数据所描述的差异表达分析在(张等人,2018)来鉴定蛋白质与显著改变归一化的蛋白质丰度在疾病状态使用±比对照组和1.3倍的变化的阈值P <0.05 .
确定疾病与对照中每个 N-糖基化位点的 N-糖基化位点占有率的倍数变化,即含N-糖基点的肽的标准化N-糖肽丰度的倍数变化除以标准化蛋白质丰度的倍数变化相应的糖蛋白。
鉴定的N- glycosites与改变的N-糖基化位点占据在使用疾病状态±1.3倍的变化的阈值在疾病相对于对照在N-糖基化位点占据。
对n N-糖基化位点是否仅在疾病或对照样品中占据进行定性评估,以识别在疾病状态下 N-糖基化完全丧失或获得的 N-糖基化位点。


鉴定差异N-糖基化蛋白和失调的N-糖基化影响过程
将疾病中的差异 N-糖基化蛋白识别为包含体内N-糖基化位点的 N-糖蛋白,其N-糖基化位点占有率发生变化和/或在疾病状态下 N-糖基化完全丧失或获得。 
将疾病中的高糖基化蛋白定义为包含N-糖基化位点的 N-糖蛋白,其N-糖基化位点占有率增加和/或在疾病状态下获得 N-糖基化。
将疾病中的低糖基化蛋白定义为包含N-糖基化位点的 N-糖蛋白,在疾病状态下 N-糖基化位点占有率降低和/或 N-糖基化完全丧失。
将异常 N-糖基化蛋白定义为包含高糖基化和低糖基化N-糖位点的N-糖蛋白。
进行基因Ó的所生成的数据集的ntology(GO)富集分析差异N-糖基化的蛋白质使用的MetaCore的生物信息学软件如所描述的在张等人。( 2020)揭示疾病状态下受d N-糖基化影响的生物过程失调。


致谢


这项工作得到了美国国立卫生研究院 (NIH)赠款 RF1 AG057965(到 LL)和R56 AG059714 (到 LSC)的支持。该协议改编自我们最近发表的研究论文( DOI: 10.1126/sciadv.abc5802) 。在埃默里中心神经退化性疾病脑库是由美国国立卫生研究院资助P50 AG025688和P30 NS055077的部分资助。


利益争夺


作者声明他们没有相互竞争的利益。


伦理


与这项工作相关的研究是根据 NIH涉及人体组织的研究指南以及赫尔辛基宣言的道德标准和原则进行的。人死后脑组织从获得的埃默里中心神经退化性疾病脑库,及脑组织被收购与机构审查委员会(IRB)从主题或批准和知情同意IR系列。


参考


Cherepanova, N.、Shrimal, S. 和 Gilmore, R.(2016 年)。内质网的 N-连接糖基化和稳态。Curr Opin Cell Biol 41:57-65。
Freeze, HH, Eklund, EA, Ng, BG 和 Patterson, MC (2015)。人类糖基化障碍的神经学方面。Annu Rev Neurosci 38:105-125。
Karpievitch, YV, Dabney, AR 和 Smith, RD (2012)。用于无标记 LC-MS 分析的归一化和缺失值插补。BMC 生物信息学13 增刊 16:S5。
Kuster, B. 和 Mann, M. (1999)。18 N-糖基化位点的 O 标记,以使用肽质量图谱和数据库搜索改进凝胶分离糖蛋白的鉴定。肛门化学71(7):1431-1440。
Ma, C., Qu, J., Meisner, J., Zhao, X., Li, X., Wu, Z., Zhu, H., Yu, Z., Li, L., Guo, Y., Song, J. 和 Wang, PG (2015)。使用微波辅助酸水解和特征离子识别绘制 N-糖基化位点的方便而精确的策略。肛门化学87(15):7833-7839。
Moremen, KW, Tiemeyer, M. 和 Nairn, AV (2012)。脊椎动物蛋白质糖基化:多样性、合成和功能。Nat Rev Mol Cell Biol 13(7): 448-462。
Pinho, SS 和 Reis, CA (2015)。癌症中的糖基化:机制和临床意义。Nat Rev Cancer 15(9): 540-555。
Reily, C.、Stewart, TJ、Renfrow, MB 和 Novak, J.(2019 年)。健康和疾病中的糖基化。Nat Rev Nephrol 15(6): 346-366。
Schedin-Weiss, S.、Winblad, B. 和 Tjernberg, LO (2014)。蛋白质糖基化在阿尔茨海默病中的作用。FEBS J 281(1): 46-62。
Wisniewski, JR (2017)。过滤辅助样品制备:蛋白质组学分析的通用和高效方法。方法 Enzymol 585:15-27。
Wisniewski, JR、Zougman, A.、Nagaraj, N. 和 Mann, M. (2009)。蛋白质组分析的通用样品制备方法。Nat 方法6(5): 359-362。
Zhang, Q.、Ma, C.、Chin, LS 和 Li, L.(2020 年)。综合糖蛋白质组学揭示阿尔茨海默病中的蛋白质 N-糖基化畸变和糖蛋白质组学网络改变。科学 Adv 6(40) 。
Zhang, Q., Ma, C., Gearing, M., Wang, PG, Chin, LS 和 Li, L. (2018)。综合蛋白质组学和网络分析可识别阿尔茨海默病中的蛋白质中心和网络改变。Acta Neuropathol Commun 6(1): 19。
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Copyright: © 2021 The Authors; exclusive licensee Bio-protocol LLC.
引用: Readers should cite both the Bio-protocol article and the original research article where this protocol was used:
  1. Zhang, Q., Ma, C., Li, L. and Chin, L. (2021). Differential Analysis of N-glycopeptide Abundance and N-glycosylation Site Occupancy for Studying Protein N-glycosylation Dysregulation in Human Disease . Bio-protocol 11(12): e4059. DOI: 10.21769/BioProtoc.4059.
  2. Zhang, Q., Ma, C., Chin, L.S. and Li, L. (2020). Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer's disease. Sci Adv 6(40).
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